Research Assistant, 3 positions / Tutkimusapulainen, 3 paikkaa (Data Science Laboratory)

Tampere University and Tampere University of Applied Sciences create a unique environment for multidisciplinary, inspirational and high-impact research and education. Our universities community has its competitive edges in technology, health and society. www.tuni.fi/en

We are seeking master’s degree students to work as Research Assistants on multiple different projects in the Data Science Laboratory at Tampere University. The positions are in the Computing Sciences unit of the Faculty of Information Technology and Communication Sciences.

JOB DESCRIPTIONS

At Tampere University, the title of Research Assistant is generally assigned to students who are both studying and working at Tampere University and perform research duties alongside their studies. Research Assistants are usually hired on a part-time contract, but they can also occupy a full-time position, for example, during the summer or while engaged in a thesis project.

We are now seeking Research Assistants to work on three specific topics in the Data Science Laboratory / Data Science Research Centre. Please, indicate in your application your top two preferred topics from the following: 

1. Inference Optimization Strategies for High-Frequency Financial Time-Series Prediction

Supervisors: Juho Kanniainen and Alexandros Iosifidis 

This project reduces inference speed of state-of-the-art machine learning models for high-frequency trading. The work studies speed–accuracy trade-offs focused on structured pruning suited to CPU/GPU dense computation. Candidate toolchains include Hugging Face Optimum (ONNX export, graph optimizations, quantization), Torch-Pruning (parameter removal and network reconstruction), NVIDIA Model Optimizer (8/4-bit precision and TensorRT integration), and PLiNIO (gradient-based compression). A three-step plan covers baselines, pruning pipeline prototyping, and real-world evaluation with a conference paper.

2. Bias-Augmented Bayesian Mallows Model for Fair Ranking Analysis

Supervisors: Henri Pesonen and Kostas Stefanidis

This project studies fairness in ranking data using a Bayesian extension of the Mallows model. The Mallows model provides a probabilistic framework centered on a latent consensus ranking. We aim to augment this model with group-level bias parameters, capturing whether certain groups are systematically advantaged or disadvantaged. The research assistant will first study and implement the Bayesian Mallows model. Next, they will extend it to include bias-related parameters and develop inference methods (e.g., MCMC) for joint estimation. Real datasets will be used for empirical evaluation. The project includes analyzing uncertainty in rankings and bias estimates. Comparisons with baseline (non-bias) models will be performed. Results will be summarized in a conference paper. 

3. Fair Recommendations for Data-Driven Groups

Supervisors: Jaakko Peltonen and Kostas Stefanidis

Fairness of recommendations over groups of users has been a prominent concern, and we have recently developed methods for multisided fair recommendation over items, consumers, and providers, considering limited item availability. However, most fairness approaches consider predefined sensitive groups of users, such as demographic categories. In this work, we will create a new fair method for recommendations that learns the groups of users from data itself in addition to any known covariates. This will ensure that consumer groups can be learned with different purchase interests (e.g., groups interested in similar specialty goods) and that they will receive fair treatment in recommendation. The work has a clear starting point from our previous fair recommendation work, which this project will extend by learning user groups. The solution can be developed either to learn the groups from data as an initial phase followed by fair recommendations, or to continue to adjust the groups while recommendation is ongoing. The project aims at submitting a paper on the new fair recommendation system to an upcoming conference.

REQUIREMENTS

Minimum formal requirement is a valid study right in a university in the field of Computing Sciences (or in a similar field). We value both practical skills in addition to good study records at Tampere University, or in another well-established university. We expect you to have the ability and high motivation for working with us. We require capability to communicate in English. 

WE OFFER

The positions are fixed-term for 2 months (full-time). The expected starting date is in August 2026, but the exact length of employment and the exact allocation (e.g., full-time or part-time) will be mutually agreed by both parties before the start of employment.

The salary will be based on both the job demands and the employee's personal performance in accordance with the University Salary System. The position of Research Assistant is placed on the job demand level 1 (the demand level chart for the teaching and research staff). In addition, employees will receive performance-based salary which for Research Assistants is based on their study credits. Full-time salary for a Research Assistant is around € 2200–2600 per month depending on completed study credits.

We offer a wide range of staff benefits, such as occupational health care services, flexible working hours, versatile research infrastructure, modern teaching facilities, excellent sports facilities on campus and several restaurants and cafés on campus with staff discounts. Please read more about working at Tampere University  and Working in Tampere, Finland.

HOW TO APPLY

Please submit your application through our online recruitment system (link below). The closing date for applications is May 18th, 2026 (at 23:59 EEST / UTC+3). Please write your application and all accompanying documents in English and attach them in PDF format.

Applications should include the following documents:

1. Motivation letter (one page PDF), underlying your current skills, main interests, and the two preferred topics you are most interested in
2. CV
3. Transcript of records (MSc students, also your BSc transcript)

Please note that applicants may be invited to participate in an interview throughout the application period and therefore we encourage you to apply as soon as possible.

For more information, contact:

Konstantinos Stefanidis, Professor of Data Science

konstantinos.stefanidis@tuni.fi

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Tampereen yliopisto ja Tampereen ammattikorkeakoulu muodostavat yhdessä Suomen toiseksi suurimman monitieteisen, innostavan ja vaikuttavan tutkimus- ja oppimisyhteisön. Korkeakouluyhteisömme osaamiskärjet ovat tekniikka, terveys ja yhteiskunta. Lue lisää: www.tuni.fi

Tampereen yliopistossa on haettavana kolme tutkimusapulaisen (Data Science Laboratory) määräaikaista tehtävää.

HAKUOHJEET

Lue tarkemmat tiedot tehtävästä ja hakuohjeet yllä olevasta englanninkielisestä ilmoituksesta.

Jätäthän hakemuksesi yliopiston sähköisellä hakulomakkeella (linkki löytyy tämän ilmoituksen alta). Hakuaika tehtävään päättyy 18.5.2026, klo 23:59.

Tagged as: Research, development & R&D